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Persian Author Classification

Project Overview

This project aims to classify texts by various Persian authors using machine learning techniques. The goal is to accurately predict the author of a given text based on its content.

Models and Techniques

  • Text Preprocessing: Tokenization, normalization, and vectorization of Persian text.
  • Machine Learning Models: Utilization of models such as SVM, Naive Bayes, and Random Forest for classification.
  • Evaluation: Accuracy, Precision, Recall, and F1 Score metrics are used to evaluate the models.

Results

The project achieved an accuracy of 70% on the testing set, with detailed performance metrics available in the report.pdf.

Repository Contents

  • persian_authors_classification.ipynb: Jupyter notebook with the main classification algorithms and model evaluations.
  • scrapper.ipynb: Jupyter notebook used for scraping textual data from various online sources.
  • report.pdf: A comprehensive report detailing the methodology, analysis, and results of the project.
  • src/: Directory containing additional source code and utility scripts supporting the project.

Installation

To set up the project environment:

git clone https://github.com/Amir-Entezari/persian-literature-classifier.git
cd persian_author_classification
pip install -r requirements.txt

Usage

To run the classification notebook:

jupyter notebook persian_authors_classification.ipynb

To execute the scraper:

jupyter notebook scrapper.ipynb

Contribution

Contributions to the project are welcome. To contribute, please fork the repository, make your changes, and submit a pull request.

Contact

For questions or feedback, please open an issue in the GitHub repository or contact [email protected] .